1. Field of Invention
The field of the present invention relates in general to image processing including image compression.
2. Description of the Related Art
Images whether obtained for civilian, medical, astronomical or military purposes comprise a grid of millions of pixels of data as to the properties of the relevant portion of the electromagnetic spectrum in which the image was obtained. Typically those properties are expressed in terms of colors in the visible spectrum. A cell phone camera is capable of 8 megapixel image capture resolution corresponding to a 3264×2448 sensor grid. A pixel is the smallest observable component of the two dimensional image plane of a digital image. Each pixel in turn may require as many as 24 bits of data to quantify the 16,777,216 possible colors of a single pixel. So the raw image captured by a cell phone may require 194,899,968 million bits of data. The shear size of the raw image presents problems for storage of the image on the cell phone or for transport of the image over the Internet. Similar considerations apply for medical and military imaging which are far larger in size.
Each raw image is compressed to reduce the storage requirements and reduce the latency associated with transport of an image. In a cell phone the compression takes place right on the phone. Image compression may be lossless or lossy, with the latter associated with far greater compression ratios.
Lossless image compression techniques like Run Length Encoding (RLE), Lempel-Ziv-Welch (LZW) and Huffman encoding, rasterize an image and categorize any redundancy in the sequence of successive colors of groups of pixels throughout the image to re-express the image in terms of symbols which represent each redundant group more compactly and which themselves are defined in an image specific lookup table or dictionary. The compressed image comprises the sequence of symbols and the associated dictionary. An image compressed using these ‘entropy’ encoding techniques exhibits no loss of information when decompressed and compared to the original raw image. One or more of these lossless techniques underpin both the popular “*.gif” and “*.png” image file formats.
Lossy image compression in general takes a far more aggressive approach to image characterization and associated compression, at a price of a loss of information with respect to the original raw image. Techniques for lossy image compression range from the simple to the complex. One example of a simple but lossy method of compression is the degradation in the color pallet of the image from 24 bit color per pixel to a single bit corresponding to a pixel color of black or white. The popular “.jpeg” image file format uses a Fourier transform and specifically the discrete cosine transform (DCT) to re-express rasterized blocks of the image in terms of the coefficients of a harmonic series with the highest frequency components of each block omitted from the compressed file. Another technique for lossy image file compression known as Principal Component Analysis (PCA) determines for each block of pixels within the image the principal component vectors, a.k.a. eigenvectors. Compression is achieved by discarding all except the most significant eigenvectors and using those principal eigenvectors to express the compressed image.
An image subject to lossy compression will have discemable differences from the original raw image. These differences increase in proportion to the compression ratio, expressed as a quotient of the raw image file size divided by the compressed image file size.
What is needed are improvements in image compression that allow increased compression ratios and resulting smaller compressed file sizes coupled with improved fidelity to the original raw image.